Predicting Public Uptake of Digital Contact Tracing During the COVID-19 Pandemic: Results From a Nationwide Survey in Singapore

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Abstract

During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low.

Objective

In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore.

Methods

A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables: (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status).

Results

Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56; P<.001). Network analyses revealed that uptake was most related to the following: using hand sanitizers, avoiding public transport, and preferring outdoor over indoor venues during the pandemic. However, demographic and situational characteristics were not significantly associated with app downloads.

Conclusions

Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps.

Trial Registration

ClinicalTrials.gov NCT04468581, https://clinicaltrials.gov/ct2/show/NCT04468581

Article activity feed

  1. SciScore for 10.1101/2020.08.26.20182386: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: Prior to study enrolment, participants provided informed consent in accordance with a protocol approved by the Yale-NUS College Ethics Review Committee (#2020-CERC-001; ClinicalTrials.gov registration: NCT04468581).
    IRB: Prior to study enrolment, participants provided informed consent in accordance with a protocol approved by the Yale-NUS College Ethics Review Committee (#2020-CERC-001; ClinicalTrials.gov registration: NCT04468581).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All data analyses were conducted via the statistical packages SPSS (Ver. 23) and R (3.6.0), with the type 1 decision-wise error rate controlled at α = 0.05.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    4.3 Limitations: In discussing our findings, we note several limitations of our study. First, our survey relied on participants’ self-reported use of a contact tracing application. Although our download rate approximates that of the general population, further studies may seek to verify actual usage - for example, by incorporating survey questions into a contact tracing application. Second, we examined TraceTogether, a centralized contact tracing protocol. Future research will need to assess whether our findings extend to decentralized protocols, or to other forms of digital contact tracing that do not use phone applications (e.g., the public acceptance of cloud-based contact in South Korea). 4.4. Conclusion: To conclude, there is growing recognition that digital technology can contribute to pandemic management. What remains unclear, however, is how this technology is received and how best to promote uptake. Focusing on contact tracing, we found that downloads of a phone application was best predicted by the adoption of other infection control measures such as increased hand hygiene. In other words, introducing digital contact tracing is not merely a call to “TraceTogether” but to “modify together”, to use contact tracing applications as part of the broader spectrum of behavioral modifications during a pandemic.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04468581CompletedCharacteristics of TraceTogether Users


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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